Theoretical and Experimental Systems Biology
John Wikswo
Spring, 2012
Undergraduates: PHYS 240 01 and BME 290B -- SpTp: Systems Biology
Graduate students: PHYS 356 Theoretical and Experimental Systems Biology
BME 395B -- SpTp: Systems Biology
Meeting times: 2:10 – 3:00 MWF in SC6837.
Systems Biology is an emergent field in science that arose from the realization that the shockwave of genetic and proteomic information must be addressed in an integrative manner. While Systems Biology has yet to have a universally acceptable definition, we adopt as a working definition “quantitative, postgenomic, postproteomic, dynamic, multiscale physiology.” Computer models are central to the field, but the greatest challenge of Systems Biology is that these models may eventually have millions or even moles of dynamic variables with complex nonlinear interactions. Historically, biological measurements have been made one at a time, every twenty minutes, suggesting that the specification of model parameters may pose a significant challenge to Systems Biology. While microarray technologies allow the measurement of the expression profiles of thousands of genes, these data are far from sufficient to specify quantitative dynamic cellular signaling and metabolic models. Hence there are major gaps between the computational vision and experimental reality that must be bridged. This course will address some of these problems.
To facilitate the discussion, we will dissect and project the multifaceted complexity of living systems as described by Systems Biology into five dimensions: (1) Molecular Complexity; (2) Structural Complexity; (3) Temporal Complexity; (4) Abstraction and Emergence; and (5) Algorithmic Complexity. This five-dimensional space will provide a reference framework for comparing, classifying and complementing the diversity of research in Systems Biology. Using this framework, we will explore the experimental and computational strategies that must evolve as Systems Biology develops into a coherent discipline. Emphasis will be directed towards the identification of techniques that might allow the simultaneous measurement and control of the dynamics of hundreds to thousands of intracellular and intercellular variables and the interpretation of the data in terms of the underlying biological systems.
This course will be structured as a seminar/journal club that will analyze in detail both the formal aspects of systems biology, and the challenges of constructing integrated microphysiological systems, i.e., multiple rgans on a chip. We will draw heavily from an on-line text (Life: An Introduction to Complex Systems Biology (Understanding Complex Systems), by Kunihko Kaneko, Springer, 2006) and approximately twenty published articles in the field. Each week, one class will be spent on a detailed analysis of a single chapter of the textbook, the second class will be spent on the analysis of one or more journal articles, and the third class on the organ-on-a-chip challenge. Instead of homework, each student will be expected to present three or more small reports and five-minute presentations during the course of the semester to address specific points of interest to that student which were raised in a prior class discussion. The final project for each student will be the preparation and presentation of an NSF graduate fellowship proposal to address one or more of challenges in experimental or theoretical systems biology, for example how to create a particular organ-on-a-chip or how to model a particular system. Because the class will contain both undergraduate and graduate students, each graduate student will be expected to serve as a lead mentor to a group of undergraduates, either through out-of-class meetings of their study group, email threads, or blogs.
In support of our study of Systems Biology, we will pay particular attention to the development of skills in critical thinking, integrative analysis, collaborative learning, peer mentoring, and classroom presentations. Students will work in self-mentored study groups to learn and present the requisite background information to the class; different, weekly groupings of students working together in class will analyze and explain subsections of the article under discussion; and individual students will prepare and present compact answers to the most pressing questions raised in the previous class. Together, the class will discuss strategies to deal with the multifaceted experiments, models, and computations that will be required to meet the challenges of Systems Biology.
The chapters of the textbook are:
1 How Should Living Systems Be Studied? . . . . . . . . . . . . . . . . . . 1
2 Constructive Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Basic Concepts in Dynamical Systems . . . . . . . . . . . . . . . . . . . . . 47
4 Origin of Bioinformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5 Origin of a Cell with Recursive Growth . . . . . . . . . . . . . . . . . . . . 111
6 Universal Statistics of a Cell with Recursive Growth . . . . . . . 135
7 Cell Differentiation and Development . . . . . . . . . . . . . . . . . . . . . . 159
8 Irreversible Differentiation from Stem Cell
9 Pattern Formation and Origin of Positional Information . . . 227
10 Genetic Evolution with Phenotypic Fluctuations . . . . . . . . . . . 255
11 Speciation as a Fixation of Phenotypic Differentiation . . . . . 281
12 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
The introduction to the Organ on a Chip concept will be provided by three Requests for Proposals from DARPA, NIH, and DTRA, and an article:
Monya Baker. Tissue models: A living system on a chip. Nature 471 (7340):661-665, 2011.
I will pick the other articles as the reading progresses. Tentatively, the first several will be:
S. Huang and J. Wikswo. Dimensions of systems biology. In: Reviews of Physiology, Biochemistry and Pharmacology, edited by S. G. Amara, E. Bamberg, T. Gudermann, S. C. Hebert, R. Jahn, W. J. Lederer, R. Lill, A. Miyajima, and S. Offermanns, 2006, p. 81-104.
S. Huang, G. Eichler, Y. Bar-Yam, and D. E. Ingber. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys.Rev.Lett. 94 (12):128701, 2005.
Hiroaki Kitano. Computational systems biology. Nature 420 (6912):206-210, 2002
J. J. Tyson, K. C. Chen, and B. Novak. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr.Opin.Cell Biol. 15 (2):221-231, 2003.
E. H. Davidson, J. P. Rast, P. Oliveri, A. Ransick, C. Calestani, C. H. Yuh, T. Minokawa, G. Amore, V. Hinman, C. Arenas-Mena, O. Otim, C. T. Brown, C. B. Livi, P. Y. Lee, R. Revilla, A. G. Rust, Z. J. Pan, M. J. Schilstra, P. J. C. Clarke, M. I. Arnone, L. Rowen, R. A. Cameron, D. R. McClay, L. Hood, and H. Bolouri. A genomic regulatory network for development. Science 295 (5560):1669-1678, 2002.
M. Kaern, T. C. Elston, W. J. Blake, and J. J. Collins. Stochasticity in gene expression: from theories to phenotypes. Nat.Rev.Genet. 6 (6):451-464, 2005.
and W. J. Blake, M. Kaern, C. R. Cantor, and J. J. Collins. Noise in eukaryotic gene expression. Nature 422 (6932):633-637, 2003.
